A QoS and Cognitive Parameters based Uncertainty Model for Selection of Semantic Web Services

Objectives: The major goal of this research paper is to present a QoS and cognitive parameter based model for selection of semantic web services. The presented model provides a completely novel and formalized measurement of different cognitive parameters. Methods/Statistical analysis: Rule based model is used for describing hierarchical relationships among QoS and cognitive parameters. The short life factor is used for dealing with known certainties lies in these parameters. The certainty factor is computed by using a measure of belief and measure of disbelief. Finally, the computed result is based on the satisfaction level of consumer agent. Findings: The rule base model generated from the hierarchal structure is used for computing CCF of each qualitative and quantitative parameter. As the rule base is generated from the hierarchical tree therefore as tree changes the rule base also changes. It is observed from the result that the overall computational overhead is very less in this cognitive based uncertainty model; it leads to fast, efficient and smart retrieval or selection of services for consumer agent. The proposed approach overcomes limitations of different models by combining several cognitive parameters, focusing on user’s preferences on QoS attributes in an efficient way. Application/Improvements: The predicted applications of proposed model in E-learning, E-governance based systems and identification of web services. The generated rule base is large so by adapting neuro symbolic rules the rule base could be reduced to provide efficient and fast delivery of services.

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